Abstract:
Due to the complexity of potential error sources in land surface models, the accurate specification of model error parameters has emerged as a major challenge in the development of effective land data assimilation systems for hydrologic and hydro-climatic applications. While several on-line procedures for estimating model error parameters - based on the statistical analysis of filtering innovations - have been introduced for geophysical models, such procedures have not been widely applied to land surface models. Consequently, little is currently known about their relative merits with regards to land surface data assimilation applications. Using a series of synthetic twin experiments and an Ensemble Kalman filter, this paper will inter-compare the performance of a number of existing adaptive filtering approaches when applied to an observation and modeling error estimation problem within a land data assimilation system. These comparisons will highlight the suitability of classical adaptive filtering approaches (designed for purely linear systems) for nonlinear land surface models. Special emphasis will also be placed on identifying the suitability of various approaches with regards to the unique characteristics of the land surface data assimilation problem (relative to similar problems in ocean and atmosphere modeling). These attributes include the high degree of land surface spatial heterogeneity which precludes the use of ergodic techniques for sampling innovation statistics and the nonlinear, yet fundamentally dissipative, structure of land surface processes. Preliminary real data results based on the assimilation of remotely-sensed surface soil moisture retrievals into a land surface model forced by satellite-based precipitation will be used to demonstrate the potential value of these approaches in an operational setting.